Machine Learning and Gravitational Wave Astronomy
Lead Research Organisation:
CARDIFF UNIVERSITY
Department Name: School of Physics and Astronomy
Abstract
In recent years machine learning techniques such as deep neural networks have shown tremendous promise in applications such a image classification. This project will explore machine learning applications in the new field of gravitational-wave astronomy, both for signal detection and background noise classification.
People |
ORCID iD |
| Patrick Sutton (Primary Supervisor) | |
| Vasileios Skliris (Student) |
Studentship Projects
| Project Reference | Relationship | Related To | Start | End | Student Name |
|---|---|---|---|---|---|
| ST/P006779/1 | 01/10/2017 | 30/09/2024 | |||
| 1946053 | Studentship | ST/P006779/1 | 01/10/2017 | 30/09/2021 | Vasileios Skliris |
| Description | Application for long term attachment costs for students starting in 2017 academic year. |
| Amount | £4,413 (GBP) |
| Funding ID | ST/P006779/1 |
| Organisation | Science and Technologies Facilities Council (STFC) |
| Sector | Public |
| Country | United Kingdom |
| Start | 06/2018 |
| End | 06/2020 |
| Description | Machine Learning and Gravitational Wave Astronomy |
| Amount | £59,108 (GBP) |
| Funding ID | 1946053 |
| Organisation | Science and Technologies Facilities Council (STFC) |
| Sector | Public |
| Country | United Kingdom |
| Start | 10/2017 |
| End | 09/2021 |
| Title | Machine Learning Data generator tool for gravitational waves |
| Description | This is a python package that generates datasets with gravitational waves and noise. It also simplifies the creation of machine learning models , their training and their testings to extract scientific information. |
| Type Of Material | Data handling & control |
| Year Produced | 2019 |
| Provided To Others? | Yes |
| Impact | Me and new students can be relieved from the time consuming and complicated procedure of making datasets from scratch. New PhD students are using my tool for similar research. |
| URL | https://github.com/VasSkliris/mly |
| Description | LIGO Scientific Collaboration |
| Organisation | LIGO Scientific Collaboration |
| Country | United States |
| Sector | Academic/University |
| PI Contribution | As my work is related to gravitational waves, I am part of LIGO Scientific collaboration. My contribution is to make machine learning algorithm that helps distinguish signals from noise in the detectors. |
| Collaborator Contribution | LIGO offers me access to their data that are not open in public, also I get involved in various projects that need attention. |
| Impact | My work combines computer science, signal processing and astrophysics. |
| Start Year | 2017 |